Field of the Invention
Embodiments of the present invention relate generally to navigation systems and, more specifically, to approaches to crowdsourced-based wait time estimates.
Description of the Related Art
A wide variety of planning tools are used to facilitate efficient time management. For example, a navigation tool may predict the quickest route to reach a point-of-interest, allowing users to minimize travel time. Further, some navigation tools allow users to search for different types of businesses and compare the distances and/or travel times between a current location and those different identified businesses. Irrespective of the specific planning information that is provided by the planning tool (i.e., an optimized route to travel, travel times to different identified businesses, etc.), such tools typically rely on predictive models to estimate portions of the planning process.
In general, the effectiveness of most planning tools is limited by the accuracy of the predictive models implemented in those tools. If portions of the planning process are not adequately addressed by the predictive models included in the planning tools, then the predictive models are undermined, and the power of the planning tool is reduced. For instance, a navigation tool that does not differentiate between the travel time to a downtown office at rush hour and the travel time to the downtown office at midnight on a Monday night can base travel time estimates on irrelevant data. Consequently, those tools can provide misleading travel time estimates.
Notably, the prediction models included in conventional planning tools typically neither directly address wait time requirements nor incorporate wait time estimates (e.g., lengths of time expected to wait in queues for services) into overall time estimates. Since many commonly planned events involve estimating wait times at points-of-sale (e.g., shopping for groceries, purchasing tickets, getting an oil change, etc.), the usefulness of conventional planning tools is limited. For instance, if the planning requirement is “estimate the quickest plan to see an emergency room doctor,” then a planning tool that does not include a predictive model for wait time fails to consider the differences between wait times at trauma centers and general hospitals. Consequently, the accuracy of the planning tool is undermined.
If a user desires wait-time guidance at a particular destination, such as a grocery store, then the user must resort to manual approaches for determining those wait times. For example, the user might have to call the destination and verbally query the workers regarding the wait time. Alternatively, some destinations and service providers provide general guidelines for wait times on their websites. For instance, airports may provide estimated wait times at security lines or for delayed flights.
While such manual techniques enable users to obtain some amount of information relevant to wait times, such manual effort is tedious for users and also can increase the time required for overall planning to unacceptable levels. Further, the manually-obtained information is not necessarily reliable or accurate and, therefore, may need to be updated frequently.
As the foregoing illustrates, more effective techniques for estimating wait times would be useful.